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Record W598301463 · doi:10.1017/s0001924000009635

A rating scale for the subjective assessment of simulation fidelity

2014· article· en· W598301463 on OpenAlexafffundabout
Philip Perfect, Emma Timson, Mark White, Gareth D. Padfield, R. Erdos, Arthur Gubbels

Bibliographic record

VenueThe Aeronautical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsNational Research Council Canada
FundersArmy Research LaboratoryEngineering and Physical Sciences Research CouncilNational Research Council Canada
KeywordsFidelityRating scaleTask (project management)Scale (ratio)Flight simulatorComputer scienceSimulationVariety (cybernetics)Adaptation (eye)High fidelityOperations researchSystems engineeringEngineeringArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Abstract A new rating scale for capturing pilot subjective assessment of simulation fidelity is described in this paper. The scale has been developed through a series of flight and simulation trials using six test pilots from a variety of backgrounds, and is based on the methodology utilised with the Cooper-Harper Handling Qualities Rating scale and the concepts of transfer of training, comparative task performance and task strategy adaptation. The development of the new rating scale has been undertaken using simulations of rotary-wing aircraft on the University of Liverpool’s HELIFLIGHT-R research simulator, in conjunction with the Canadian Flight Research Laboratory’s Bell 412 ASRA in-flight simulator. The utility of the scale applied to locating fidelity boundaries for quantitative metrics is illustrated for an inter-axis coupling criterion. The work described in this paper is preliminary in nature, and research activities are on-going to continue the validation of the fidelity rating scale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.290
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations34
Published2014
Admission routes3
Has abstractyes

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